{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "colab": {
      "name": "CS-DQN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "btpip0ixUl_c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fdb5b80c-6bee-47d7-f158-cb688724c9bd"
      },
      "source": [
        "import gym\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import deque\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import random\n",
        "\n",
        "from gym import wrappers\n",
        "envCartPole = gym.make('CartPole-v1')\n",
        "envCartPole.seed(50) #Set the seed to keep the environment consistent across runs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[50]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yM8KTrAEUmA4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Global Variables\n",
        "EPISODES = 500\n",
        "TRAIN_END = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agAQlTehUmBE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Hyper Parameters\n",
        "def discount_rate(): #Gamma\n",
        "    return 0.95\n",
        "\n",
        "def learning_rate(): #Alpha\n",
        "    return 0.001\n",
        "\n",
        "def batch_size(): #Size of the batch used in the experience replay\n",
        "    return 24"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3MYaBiXUmBM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DeepQNetwork():\n",
        "    def __init__(self, states, actions, alpha, gamma, epsilon,epsilon_min, epsilon_decay):\n",
        "        self.nS = states\n",
        "        self.nA = actions\n",
        "        self.memory = deque([], maxlen=2500)\n",
        "        self.alpha = alpha\n",
        "        self.gamma = gamma\n",
        "        #Explore/Exploit\n",
        "        self.epsilon = epsilon\n",
        "        self.epsilon_min = epsilon_min\n",
        "        self.epsilon_decay = epsilon_decay\n",
        "        self.model = self.build_model()\n",
        "        self.loss = []\n",
        "        \n",
        "    def build_model(self):\n",
        "        model = keras.Sequential() #linear stack of layers https://keras.io/models/sequential/\n",
        "        model.add(keras.layers.Dense(24, input_dim=self.nS, activation='relu')) #[Input] -> Layer 1\n",
        "        #   Dense: Densely connected layer https://keras.io/layers/core/\n",
        "        #   24: Number of neurons\n",
        "        #   input_dim: Number of input variables\n",
        "        #   activation: Rectified Linear Unit (relu) ranges >= 0\n",
        "        model.add(keras.layers.Dense(24, activation='relu')) #Layer 2 -> 3\n",
        "        model.add(keras.layers.Dense(self.nA, activation='linear')) #Layer 3 -> [output]\n",
        "        #   Size has to match the output (different actions)\n",
        "        #   Linear activation on the last layer\n",
        "        model.compile(loss='mean_squared_error', #Loss function: Mean Squared Error\n",
        "                      optimizer=keras.optimizers.Adam(lr=self.alpha)) #Optimaizer: Adam (Feel free to check other options)\n",
        "        return model\n",
        "\n",
        "    def action(self, state):\n",
        "        if np.random.rand() <= self.epsilon:\n",
        "            return random.randrange(self.nA) #Explore\n",
        "        action_vals = self.model.predict(state) #Exploit: Use the NN to predict the correct action from this state\n",
        "        return np.argmax(action_vals[0])\n",
        "\n",
        "    def test_action(self, state): #Exploit\n",
        "        action_vals = self.model.predict(state)\n",
        "        return np.argmax(action_vals[0])\n",
        "\n",
        "    def store(self, state, action, reward, nstate, done):\n",
        "        #Store the experience in memory\n",
        "        self.memory.append( (state, action, reward, nstate, done) )\n",
        "\n",
        "    def experience_replay(self, batch_size):\n",
        "        #Execute the experience replay\n",
        "        minibatch = random.sample( self.memory, batch_size ) #Randomly sample from memory\n",
        "\n",
        "        #Convert to numpy for speed by vectorization\n",
        "        x = []\n",
        "        y = []\n",
        "        np_array = np.array(minibatch)\n",
        "        st = np.zeros((0,self.nS)) #States\n",
        "        nst = np.zeros( (0,self.nS) )#Next States\n",
        "        for i in range(len(np_array)): #Creating the state and next state np arrays\n",
        "            st = np.append( st, np_array[i,0], axis=0)\n",
        "            nst = np.append( nst, np_array[i,3], axis=0)\n",
        "        st_predict = self.model.predict(st) #Here is the speedup! I can predict on the ENTIRE batch\n",
        "        nst_predict = self.model.predict(nst)\n",
        "        index = 0\n",
        "        for state, action, reward, nstate, done in minibatch:\n",
        "            x.append(state)\n",
        "            #Predict from state\n",
        "            nst_action_predict_model = nst_predict[index]\n",
        "            if done == True: #Terminal: Just assign reward much like {* (not done) - QB[state][action]}\n",
        "                target = reward\n",
        "            else:   #Non terminal\n",
        "                target = reward + self.gamma * np.amax(nst_action_predict_model)\n",
        "            target_f = st_predict[index]\n",
        "            target_f[action] = target\n",
        "            y.append(target_f)\n",
        "            index += 1\n",
        "        #Reshape for Keras Fit\n",
        "        x_reshape = np.array(x).reshape(batch_size,self.nS)\n",
        "        y_reshape = np.array(y)\n",
        "        epoch_count = 1 #Epochs is the number or iterations\n",
        "        hist = self.model.fit(x_reshape, y_reshape, epochs=epoch_count, verbose=0)\n",
        "        #Graph Losses\n",
        "        for i in range(epoch_count):\n",
        "            self.loss.append( hist.history['loss'][i] )\n",
        "        #Decay Epsilon\n",
        "        if self.epsilon > self.epsilon_min:\n",
        "            self.epsilon *= self.epsilon_decay"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGOv_PhSUmBY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create the agent\n",
        "nS = envCartPole.observation_space.shape[0] #This is only 4\n",
        "nA = envCartPole.action_space.n #Actions\n",
        "dqn1 = DeepQNetwork(nS, nA, learning_rate(), discount_rate(), 1, 0.005, 0.995 )\n",
        "dqn2 = DeepQNetwork(nS, nA, learning_rate(), discount_rate(), 1, 0.005, 0.995 )\n",
        "\n",
        "batch_size = batch_size()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uT9QeK_QUmBf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8267379a-0954-48a2-b491-9cd8848ea12d"
      },
      "source": [
        "#Training\n",
        "rewards = [] #Store rewards for graphing\n",
        "epsilons = [] # Store the Explore/Exploit\n",
        "TEST_Episodes = 0\n",
        "for e in range(EPISODES):\n",
        "    state = envCartPole.reset()\n",
        "    state = np.reshape(state, [1, nS]) # Resize to store in memory to pass to .predict\n",
        "    tot_rewards = 0\n",
        "    prev_action=0;\n",
        "    for time in range(210): #200 is when you \"solve\" the game. This can continue forever as far as I know\n",
        "        action1 = dqn1.action(state)\n",
        "        action2 = dqn2.action(state)\n",
        "        if ((action1==0 and action2==0) or (action1==1 and action2==1)):\n",
        "            action = prev_action\n",
        "        elif (action1==1 and action2==0):   \n",
        "            action = 0\n",
        "        else:\n",
        "            action = 1  \n",
        "        prev_action = action      \n",
        "        nstate, reward, done, _ = envCartPole.step(action)\n",
        "        nstate = np.reshape(nstate, [1, nS])\n",
        "        tot_rewards += reward\n",
        "        dqn1.store(state, action1, reward, nstate, done) # Resize to store in memory to pass to .predict\n",
        "        dqn2.store(state, action2, reward, nstate, done) # Resize to store in memory to pass to .predict\n",
        "        state = nstate\n",
        "        #done: CartPole fell. \n",
        "        #time == 209: CartPole stayed upright\n",
        "        if done or time == 209:\n",
        "            rewards.append(tot_rewards)\n",
        "            epsilons.append(dqn1.epsilon)\n",
        "            print(\"episode: {}/{}, score: {}, e: {}\"\n",
        "                  .format(e, EPISODES, tot_rewards, dqn1.epsilon))\n",
        "            break\n",
        "        #Experience Replay\n",
        "        if len(dqn1.memory) > batch_size:\n",
        "            dqn1.experience_replay(batch_size)\n",
        "        if len(dqn2.memory) > batch_size:\n",
        "            dqn2.experience_replay(batch_size)\n",
        "    #If our current NN passes we are done\n",
        "    #I am going to use the last 5 runs\n",
        "    if len(rewards) > 5 and np.average(rewards[-5:]) > 195:\n",
        "        #Set the rest of the EPISODES for testing\n",
        "        TEST_Episodes = EPISODES - e\n",
        "        TRAIN_END = e\n",
        "        break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "episode: 0/500, score: 8.0, e: 1\n",
            "episode: 1/500, score: 9.0, e: 1\n",
            "episode: 2/500, score: 20.0, e: 0.9416228069143757\n",
            "episode: 3/500, score: 15.0, e: 0.8778091417340573\n",
            "episode: 4/500, score: 18.0, e: 0.8061065909263957\n",
            "episode: 5/500, score: 10.0, e: 0.7705488893118823\n",
            "episode: 6/500, score: 8.0, e: 0.7439808620067382\n",
            "episode: 7/500, score: 10.0, e: 0.7111635524897149\n",
            "episode: 8/500, score: 19.0, e: 0.6498078359349755\n",
            "episode: 9/500, score: 13.0, e: 0.6118738784280476\n",
            "episode: 10/500, score: 10.0, e: 0.5848838636585911\n",
            "episode: 11/500, score: 9.0, e: 0.5618938591163328\n",
            "episode: 12/500, score: 9.0, e: 0.5398075216808175\n",
            "episode: 13/500, score: 25.0, e: 0.47862223409330756\n",
            "episode: 14/500, score: 8.0, e: 0.46211964903917074\n",
            "episode: 15/500, score: 11.0, e: 0.43952667968844233\n",
            "episode: 16/500, score: 10.0, e: 0.42013897252428334\n",
            "episode: 17/500, score: 9.0, e: 0.4036245882390106\n",
            "episode: 18/500, score: 10.0, e: 0.3858205374665315\n",
            "episode: 19/500, score: 10.0, e: 0.36880183088056995\n",
            "episode: 20/500, score: 11.0, e: 0.3507711574848344\n",
            "episode: 21/500, score: 8.0, e: 0.3386767948568688\n",
            "episode: 22/500, score: 10.0, e: 0.3237376186352221\n",
            "episode: 23/500, score: 10.0, e: 0.30945741577570285\n",
            "episode: 24/500, score: 10.0, e: 0.29580711868545667\n",
            "episode: 25/500, score: 9.0, e: 0.28417984116121187\n",
            "episode: 26/500, score: 9.0, e: 0.2730095965279488\n",
            "episode: 27/500, score: 9.0, e: 0.26227842021373715\n",
            "episode: 28/500, score: 10.0, e: 0.2507092085103961\n",
            "episode: 29/500, score: 10.0, e: 0.23965031961336\n",
            "episode: 30/500, score: 10.0, e: 0.2290792429684691\n",
            "episode: 31/500, score: 9.0, e: 0.22007483514733558\n",
            "episode: 32/500, score: 10.0, e: 0.21036724137609603\n",
            "episode: 33/500, score: 10.0, e: 0.2010878536592394\n",
            "episode: 34/500, score: 10.0, e: 0.192217783647157\n",
            "episode: 35/500, score: 38.0, e: 0.15967890763313974\n",
            "episode: 36/500, score: 11.0, e: 0.1518722266715875\n",
            "episode: 37/500, score: 8.0, e: 0.14663577052834775\n",
            "episode: 38/500, score: 9.0, e: 0.14087196468590776\n",
            "episode: 39/500, score: 10.0, e: 0.1346580429260134\n",
            "episode: 40/500, score: 12.0, e: 0.12743425563174798\n",
            "episode: 41/500, score: 15.0, e: 0.11879805134519765\n",
            "episode: 42/500, score: 9.0, e: 0.11412846151764894\n",
            "episode: 43/500, score: 14.0, e: 0.10692863247165257\n",
            "episode: 44/500, score: 35.0, e: 0.09017346495423652\n",
            "episode: 45/500, score: 58.0, e: 0.0677632708131484\n",
            "episode: 46/500, score: 11.0, e: 0.06445033334388098\n",
            "episode: 47/500, score: 27.0, e: 0.056575091797066025\n",
            "episode: 48/500, score: 10.0, e: 0.05407954064343768\n",
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            "episode: 50/500, score: 10.0, e: 0.0494138221100385\n",
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            "episode: 54/500, score: 8.0, e: 0.03826710124979409\n",
            "episode: 55/500, score: 10.0, e: 0.03657912327863173\n",
            "episode: 56/500, score: 11.0, e: 0.03479077471386296\n",
            "episode: 57/500, score: 9.0, e: 0.033423255248208356\n",
            "episode: 58/500, score: 9.0, e: 0.032109488810599975\n",
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            "episode: 62/500, score: 8.0, e: 0.027214167668287145\n",
            "episode: 63/500, score: 8.0, e: 0.026275840769466357\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRiFK9DpUmBq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a81068a3-3501-448b-8e14-83fe5f18abbd"
      },
      "source": [
        "#Test the agent that was trained\n",
        "#   In this section we ALWAYS use exploit don't train any more\n",
        "for e_test in range(TEST_Episodes):\n",
        "    state = envCartPole.reset()\n",
        "    state = np.reshape(state, [1, nS])\n",
        "    tot_rewards = 0\n",
        "    prev_action=0\n",
        "    for t_test in range(210):\n",
        "        action1 = dqn1.test_action(state)\n",
        "        action2 = dqn2.test_action(state)\n",
        "        if ((action1==0 and action2==0) or (action1==1 and action2==1)):\n",
        "            action = prev_action\n",
        "        elif (action1==1 and action2==0):   \n",
        "            action = 0\n",
        "        else:\n",
        "            action = 1  \n",
        "        prev_action = action      \n",
        " \n",
        " #       action = dqn.test_action(state)\n",
        "        nstate, reward, done, _ = envCartPole.step(action)\n",
        "        nstate = np.reshape( nstate, [1, nS])\n",
        "        tot_rewards += reward\n",
        "        #DON'T STORE ANYTHING DURING TESTING\n",
        "        state = nstate\n",
        "        #done: CartPole fell. \n",
        "        #t_test == 209: CartPole stayed upright\n",
        "        if done or t_test == 209: \n",
        "            rewards.append(tot_rewards)\n",
        "            epsilons.append(0) #We are doing full exploit\n",
        "            print(\"episode: {}/{}, score: {}, e: {}\"\n",
        "                  .format(e_test, TEST_Episodes, tot_rewards, 0))\n",
        "            break;"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "episode: 0/375, score: 210.0, e: 0\n",
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            "episode: 340/375, score: 210.0, e: 0\n",
            "episode: 341/375, score: 210.0, e: 0\n",
            "episode: 342/375, score: 210.0, e: 0\n",
            "episode: 343/375, score: 210.0, e: 0\n",
            "episode: 344/375, score: 210.0, e: 0\n",
            "episode: 345/375, score: 210.0, e: 0\n",
            "episode: 346/375, score: 210.0, e: 0\n",
            "episode: 347/375, score: 210.0, e: 0\n",
            "episode: 348/375, score: 210.0, e: 0\n",
            "episode: 349/375, score: 210.0, e: 0\n",
            "episode: 350/375, score: 210.0, e: 0\n",
            "episode: 351/375, score: 210.0, e: 0\n",
            "episode: 352/375, score: 210.0, e: 0\n",
            "episode: 353/375, score: 210.0, e: 0\n",
            "episode: 354/375, score: 210.0, e: 0\n",
            "episode: 355/375, score: 210.0, e: 0\n",
            "episode: 356/375, score: 210.0, e: 0\n",
            "episode: 357/375, score: 210.0, e: 0\n",
            "episode: 358/375, score: 210.0, e: 0\n",
            "episode: 359/375, score: 210.0, e: 0\n",
            "episode: 360/375, score: 210.0, e: 0\n",
            "episode: 361/375, score: 210.0, e: 0\n",
            "episode: 362/375, score: 210.0, e: 0\n",
            "episode: 363/375, score: 210.0, e: 0\n",
            "episode: 364/375, score: 210.0, e: 0\n",
            "episode: 365/375, score: 210.0, e: 0\n",
            "episode: 366/375, score: 210.0, e: 0\n",
            "episode: 367/375, score: 210.0, e: 0\n",
            "episode: 368/375, score: 210.0, e: 0\n",
            "episode: 369/375, score: 210.0, e: 0\n",
            "episode: 370/375, score: 210.0, e: 0\n",
            "episode: 371/375, score: 210.0, e: 0\n",
            "episode: 372/375, score: 210.0, e: 0\n",
            "episode: 373/375, score: 210.0, e: 0\n",
            "episode: 374/375, score: 210.0, e: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xu5YIAxuUmBx",
        "colab_type": "text"
      },
      "source": [
        "**Results**  \n",
        "Here is a graph of the results. If everything was done correctly you should see the rewards over the red line.  \n",
        "\n",
        "Black: This is the 100 episode rolling average  \n",
        "Red: This is the \"solved\" line at 195  \n",
        "Blue: This is the reward for each episode  \n",
        "Green: This is the value of epsilon scaled by 200  \n",
        "Yellow: This is where the tests started."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tmrdKzWkUmB0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "380f1d93-09df-4545-e2ac-eb867bbed2f6"
      },
      "source": [
        "\"\"\"\n",
        "rolling_average = np.convolve(rewards, np.ones(100)/100)\n",
        "\n",
        "plt.plot(rewards)\n",
        "plt.plot(rolling_average, color='black')\n",
        "plt.axhline(y=195, color='r', linestyle='-') #Solved Line\n",
        "#Scale Epsilon (0.001 - 1.0) to match reward (0 - 200) range\n",
        "eps_graph = [200*x for x in epsilons]\n",
        "plt.plot(eps_graph, color='g', linestyle='-')\n",
        "#Plot the line where TESTING begins\n",
        "plt.axvline(x=TRAIN_END, color='y', linestyle='-')\n",
        "plt.xlim( (0,EPISODES) )\n",
        "plt.ylim( (0,220) )\n",
        "plt.show()\n",
        "\n",
        "\n",
        "envCartPole.close()\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"\\nrolling_average = np.convolve(rewards, np.ones(100)/100)\\n\\nplt.plot(rewards)\\nplt.plot(rolling_average, color='black')\\nplt.axhline(y=195, color='r', linestyle='-') #Solved Line\\n#Scale Epsilon (0.001 - 1.0) to match reward (0 - 200) range\\neps_graph = [200*x for x in epsilons]\\nplt.plot(eps_graph, color='g', linestyle='-')\\n#Plot the line where TESTING begins\\nplt.axvline(x=TRAIN_END, color='y', linestyle='-')\\nplt.xlim( (0,EPISODES) )\\nplt.ylim( (0,220) )\\nplt.show()\\n\\n\\nenvCartPole.close()\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n99kRKN7mnWY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "9ae18674-4bfa-4698-fe0e-cf67eaf1e21f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "#np.save('/content/drive/My Drive/db/dqn/rewards_cs1',rewards)\n",
        "#np.save('/content/drive/My Drive/db/dqn/TRAIN_END_cs1',TRAIN_END)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYkgqo5CCEjU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "97551e60-4733-40e9-e840-beef2208db03"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(10,5),dpi=500)\n",
        "\n",
        "r_old=np.load('/content/drive/My Drive/db/dqn/rewards.npy')\n",
        "r_new=np.load('/content/drive/My Drive/db/dqn/rewards_cs1.npy')\n",
        "TE_old=np.load('/content/drive/My Drive/db/dqn/TRAIN_END.npy')\n",
        "TE_new=np.load('/content/drive/My Drive/db/dqn/TRAIN_END_cs1.npy')\n",
        "\n",
        "#rolling_average1 = np.convolve(r_old, np.ones(100)/100)\n",
        "#rolling_average2 = np.convolve(r_new, np.ones(100)/100)\n",
        "\n",
        "plt.plot(r_old, color= 'lightblue',label='DQN')\n",
        "plt.plot(r_new, color='steelblue',label='CS-DQN')\n",
        "#plt.plot(rolling_average1, color='grey')\n",
        "#plt.plot(rolling_average1, color='black')\n",
        "plt.axhline(y=195, color='r', linestyle='-') #Solved Line\n",
        "#Scale Epsilon (0.001 - 1.0) to match reward (0 - 200) range\n",
        "#eps_graph = [200*x for x in epsilons]\n",
        "#plt.plot(eps_graph, color='g', linestyle='-')\n",
        "#Plot the line where TESTING begins\n",
        "plt.axvline(x=TE_old, color='sandybrown', linestyle='-')\n",
        "plt.axvline(x=TE_new, color='saddlebrown', linestyle='-')\n",
        "plt.xlim( (0,200) )\n",
        "plt.ylim( (0,220) )\n",
        "plt.ylabel('Rewards')\n",
        "plt.title('Episodes')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 5000x2500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0elKpgePUmB9",
        "colab_type": "text"
      },
      "source": [
        "**Changes**  \n",
        "*hyper parameters*: You can alter alpha, gamma, batch size, and episode length to see what differences the algorithm returns.  \n",
        "*Training End*: You can also change the line where I only check the last 5 runs before switching to testing mode (if len(rewards) > 5 and np.average(rewards[-5:]) > 195:) as that doesn't prove it was solved. The reason I did this was because I wanted to limit the amount of runs I made.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B19ILOAfUmB-",
        "colab_type": "text"
      },
      "source": [
        "**Conclusion**  \n",
        "This is a Deep Q-Network implementation. There are some changes you can make here and there but it follows the paper. Hopefully, you were able to understand the code as well as make your own version to compare with this one."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "AMyF_HBtUmB_",
        "colab_type": "text"
      },
      "source": [
        "**Reference**  \n",
        "Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Petersen, S. (2015). *Human-level control through deep reinforcement learning*. Nature, 518(7540), 529"
      ]
    }
  ]
}